Inelastic collisions in molecular nitrogen at low temperature (2<T<50 K)

Theory and experiment are combined in a novel approach aimed at establishing a set of two-body state-to-state rates for elementary processes ij->lm in low temperature N2:N2 collisions involving the rotational states i, j, l, m. First, a set of 148 collision cross sections is calculated as a function of the collision energy at the converged close-coupled level via the MOLSCAT code, using a recent potential energy surface for N2–N2. Then, the corresponding rates for the range of 2<T<50 K are derived from the cross sections. The link between theory and experiment, aimed at assessing the calculated rates, is a master equation which accounts for the time evolution of rotational populations in a reference volume of gas in terms of the collision rates. In the experiment, the evolution of rotational populations is measured by Raman spectroscopy in a tiny reference volume 2E-3 mm3 of N2 traveling along the axis of a supersonic jet. The calculated collisional rates are assessed experimentally in the range of 4<T<35 K by means of the master equation, and then are scaled by averaging over a large set of experimental data. The scaled rates account accurately for the evolution of the rotational populations measured in a wide range of conditions. Accuracy of 10% is estimated for the main scaled rates.This work has been supported by the Spanish Ministerio de Educación y Ciencia, research Project Nos. FIS2004-02576, HF2004-232, ESP2004-21060-E, and ASTROCAM network. J.P.F. is indebted to the CSIC for an I3P grant.Peer reviewe

Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning

In this paper, the application of quantum simulations and quantum machine learning to solve low-energy nuclear physics problems is explored. The use of quantum computing to deal with nuclear physics problems is, in general, in its infancy and, in particular, the use of quantum machine learning in the realm of nuclear physics at low energy is almost nonexistent. We present here three specific examples where the use of quantum computing and quantum machine learning provides, or could provide in the future, a possible computational advantage: i) the determination of the phase/shape in schematic nuclear models, ii) the calculation of the ground state energy of a nuclear shell model-type Hamiltonian and iii) the identification of particles or the determination of trajectories in nuclear physics experiments.Comment: Submitted to the special issue "Quantum Machine Learning" of the journal Advanced Quantum Technologie

Predicting the spread of epidemiological diseases by using a multi-objective algorithm

The epidemiological models are able to predict the spread of diseases, but a previous work on calibrating some involved parameters must be done. In this work, we propose a methodology to adjust those parameters based on solving a multi-objective optimization problem whose objective functions measure the accuracy of the model. More precisely, we have considered the Between-Countries Disease Spread model because it involves a set of countries taking into account the migratory movements among them. As a result, using some real data about the number of detected cases and the number of deaths for the Ebola virus disease, we have shown that the methodology is able to find a set of values for the parameters so that the model fits the outbreak spread for a set of countries

Analyzing the House Fly's Exploratory Behavior with Autoregression Methods

This paper presents a detailed characterization of the trajectory of a single housefly with free range of a square cage. The trajectory of the fly was recorded and transformed into a time series, which was fully analyzed using an autoregressive model, which describes a stationary time series by a linear regression of prior state values with the white noise. The main discovery was that the fly switched styles of motion from a low dimensional regular pattern to a higher dimensional disordered pattern. This discovered exploratory behavior is, irrespective of the presence of food, characterized by anomalous diffusion.Comment: 20 pages, 9 figures, 1 table, full pape

Changes in cannabinoid receptor binding and mRNA levels in several brain regions of aged rats

AbstractWe have recently found that cannabinoid receptor binding and gene expression markedly decreased in extrapyramidal structures of aged rats. The present study was designed to analyze the possible existence of similar aging-induced changes in cannabinoid receptor binding and gene expression in brain regions other than extrapyramidal areas, but that also contain a significant population of cannabinoid receptors, such as the cerebellum, hippocampal structures, limbic and hypothalamic nuclei, the cerebral cortex and others. To this end, we analyzed cannabinoid receptor binding, using autoradiography, and cannabinoid receptor mRNA levels, using in situ hybridization, in slide-mounted brain sections obtained from young (3 month old) and aged (>2 year old) rats. Results were as follows. In the cerebellum, aged rats exhibited a marked decrease in cannabinoid receptor binding in the molecular layer (−33.3%), although accompanied by no changes in mRNA levels in the granular layer. In the cerebral cortex, a small, although statistically significant, decrease in binding was found in the deep layer (VI) (−18.3%) of aged rats, whereas no changes were found in the superficial layer (I). As in the case of the cerebellum, mRNA levels did not change in the cerebral cortex layers (II–III and V–VI). The different regions of the Ammon’s horn of the hippocampus exhibited similar cannabinoid receptor binding levels in aged and young rats. Interestingly, mRNA levels decreased in aged rats to a small, but statistically significant, extent (CA1: −26.1%; CA2: −21.6%; CA3: −14.4%). This was also seen in another hippocampal structure, the dentate gyrus (−14.6%), although in this region binding levels increased in aged rats (+28.4%). Two hypothalamic structures, the arcuate nucleus and the ventromedial hypothalamic nucleus, exhibited decreased cannabinoid receptor binding in aged rats (−31.1% and −30.3%, respectively), but this was not seen in the medial preoptic area. This was accompanied by no changes in mRNA levels in the ventromedial hypothalamic nucleus. In the limbic structures, aged rats exhibited similar binding levels to young rats. This was seen in the nucleus accumbens, septum nuclei and basolateral amygdaloid nucleus. However, mRNA levels slightly decreased in the basolateral amygdaloid nucleus (−13.4%), whereas they were not altered in the septum nuclei. Finally, other brain structures, such as the central gray substance and the brainstem, exhibited similar binding levels in aged and young rats. However, it is important to note that mRNA levels increased significantly (+211.2%) in the brainstem of aged rats, an area where the levels of binding and mRNA were very low in young rats. This marked increase may be related to an increase in the presence of glial elements in this region, as revealed by the increase in the immunoreactivity for glial fibrillary acidic protein observed in the brainstem of aged rats as compared to young animals. In summary, senescence was associated with changes in cannabinoid receptors in the cerebellum, the cerebral cortex, limbic and hypothalamic structures, the hippocampus and other brain regions. However, the changes observed (i) were not as marked and relevant as those early reported in extrapyramidal areas, and (ii) exhibited regional differences that might be attributed to the different roles played by these receptors in each region. Of particular relevance by their magnitude were the aging-induced decrease in binding found in the cerebellum and the hypothalamus, and the increase in mRNA levels observed in the brainstem. The latter might be related to an increase in the presence of glial cells which might contain cannabinoid receptor mRNA